import audioread
import librosa
import numpy as np
import soundfile as sf
import math
import platform
import traceback
from audio_separator.separator.uvr_lib_v5 import pyrb
from scipy.signal import correlate, hilbert
import io

OPERATING_SYSTEM = platform.system()
SYSTEM_ARCH = platform.platform()
SYSTEM_PROC = platform.processor()
ARM = "arm"

AUTO_PHASE = "Automatic"
POSITIVE_PHASE = "Positive Phase"
NEGATIVE_PHASE = "Negative Phase"
NONE_P = ("None",)
LOW_P = ("Shifts: Low",)
MED_P = ("Shifts: Medium",)
HIGH_P = ("Shifts: High",)
VHIGH_P = "Shifts: Very High"
MAXIMUM_P = "Shifts: Maximum"

progress_value = 0
last_update_time = 0
is_macos = False


if OPERATING_SYSTEM == "Darwin":
    wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else "sinc_fastest"
    wav_resolution_float_resampling = "kaiser_best" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else wav_resolution
    is_macos = True
else:
    wav_resolution = "sinc_fastest"
    wav_resolution_float_resampling = wav_resolution

MAX_SPEC = "Max Spec"
MIN_SPEC = "Min Spec"
LIN_ENSE = "Linear Ensemble"

MAX_WAV = MAX_SPEC
MIN_WAV = MIN_SPEC

AVERAGE = "Average"


def crop_center(h1, h2):
    """
    This function crops the center of the first input tensor to match the size of the second input tensor.
    It is used to ensure that the two tensors have the same size in the time dimension.
    """
    h1_shape = h1.size()
    h2_shape = h2.size()

    # If the time dimensions are already equal, return the first tensor as is
    if h1_shape[3] == h2_shape[3]:
        return h1
    # If the time dimension of the first tensor is smaller, raise an error
    elif h1_shape[3] < h2_shape[3]:
        raise ValueError("h1_shape[3] must be greater than h2_shape[3]")

    # Calculate the start and end indices for cropping
    s_time = (h1_shape[3] - h2_shape[3]) // 2
    e_time = s_time + h2_shape[3]
    # Crop the first tensor
    h1 = h1[:, :, :, s_time:e_time]

    return h1


def preprocess(X_spec):
    """
    This function preprocesses a spectrogram by separating it into magnitude and phase components.
    This is a common preprocessing step in audio processing tasks.
    """
    X_mag = np.abs(X_spec)
    X_phase = np.angle(X_spec)

    return X_mag, X_phase


def make_padding(width, cropsize, offset):
    """
    This function calculates the padding needed to make the width of an image divisible by the crop size.
    It is used in the process of splitting an image into smaller patches.
    """
    left = offset
    roi_size = cropsize - offset * 2
    if roi_size == 0:
        roi_size = cropsize
    right = roi_size - (width % roi_size) + left

    return left, right, roi_size


def normalize(wave, max_peak=1.0):
    """Normalize audio waveform to a specified peak value.

    Args:
        wave (array-like): Audio waveform.
        max_peak (float): Maximum peak value for normalization.

    Returns:
        array-like: Normalized or original waveform.
    """
    maxv = np.abs(wave).max()
    if maxv > max_peak:
        wave *= max_peak / maxv

    return wave


def auto_transpose(audio_array: np.ndarray):
    """
    Ensure that the audio array is in the (channels, samples) format.

    Parameters:
        audio_array (ndarray): Input audio array.

    Returns:
        ndarray: Transposed audio array if necessary.
    """

    # If the second dimension is 2 (indicating stereo channels), transpose the array
    if audio_array.shape[1] == 2:
        return audio_array.T
    return audio_array


def write_array_to_mem(audio_data, subtype):
    if isinstance(audio_data, np.ndarray):
        audio_buffer = io.BytesIO()
        sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format="WAV")
        audio_buffer.seek(0)
        return audio_buffer
    else:
        return audio_data


def spectrogram_to_image(spec, mode="magnitude"):
    if mode == "magnitude":
        if np.iscomplexobj(spec):
            y = np.abs(spec)
        else:
            y = spec
        y = np.log10(y**2 + 1e-8)
    elif mode == "phase":
        if np.iscomplexobj(spec):
            y = np.angle(spec)
        else:
            y = spec

    y -= y.min()
    y *= 255 / y.max()
    img = np.uint8(y)

    if y.ndim == 3:
        img = img.transpose(1, 2, 0)
        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)

    return img


def reduce_vocal_aggressively(X, y, softmask):
    v = X - y
    y_mag_tmp = np.abs(y)
    v_mag_tmp = np.abs(v)

    v_mask = v_mag_tmp > y_mag_tmp
    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)

    return y_mag * np.exp(1.0j * np.angle(y))


def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
    mask = y_mask

    try:
        if min_range < fade_size * 2:
            raise ValueError("min_range must be >= fade_size * 2")

        idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
        start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
        end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
        artifact_idx = np.where(end_idx - start_idx > min_range)[0]
        weight = np.zeros_like(y_mask)
        if len(artifact_idx) > 0:
            start_idx = start_idx[artifact_idx]
            end_idx = end_idx[artifact_idx]
            old_e = None
            for s, e in zip(start_idx, end_idx):
                if old_e is not None and s - old_e < fade_size:
                    s = old_e - fade_size * 2

                if s != 0:
                    weight[:, :, s : s + fade_size] = np.linspace(0, 1, fade_size)
                else:
                    s -= fade_size

                if e != y_mask.shape[2]:
                    weight[:, :, e - fade_size : e] = np.linspace(1, 0, fade_size)
                else:
                    e += fade_size

                weight[:, :, s + fade_size : e - fade_size] = 1
                old_e = e

        v_mask = 1 - y_mask
        y_mask += weight * v_mask

        mask = y_mask
    except Exception as e:
        error_name = f"{type(e).__name__}"
        traceback_text = "".join(traceback.format_tb(e.__traceback__))
        message = f'{error_name}: "{e}"\n{traceback_text}"'
        print("Post Process Failed: ", message)

    return mask


def align_wave_head_and_tail(a, b):
    l = min([a[0].size, b[0].size])

    return a[:l, :l], b[:l, :l]


def convert_channels(spec, mp, band):
    cc = mp.param["band"][band].get("convert_channels")

    if "mid_side_c" == cc:
        spec_left = np.add(spec[0], spec[1] * 0.25)
        spec_right = np.subtract(spec[1], spec[0] * 0.25)
    elif "mid_side" == cc:
        spec_left = np.add(spec[0], spec[1]) / 2
        spec_right = np.subtract(spec[0], spec[1])
    elif "stereo_n" == cc:
        spec_left = np.add(spec[0], spec[1] * 0.25) / 0.9375
        spec_right = np.add(spec[1], spec[0] * 0.25) / 0.9375
    else:
        return spec

    return np.asfortranarray([spec_left, spec_right])


def combine_spectrograms(specs, mp, is_v51_model=False):
    l = min([specs[i].shape[2] for i in specs])
    spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
    offset = 0
    bands_n = len(mp.param["band"])

    for d in range(1, bands_n + 1):
        h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
        spec_c[:, offset : offset + h, :l] = specs[d][:, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l]
        offset += h

    if offset > mp.param["bins"]:
        raise ValueError("Too much bins")

    # lowpass fiter

    if mp.param["pre_filter_start"] > 0:
        if is_v51_model:
            spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param["pre_filter_start"], mp.param["pre_filter_stop"])
        else:
            if bands_n == 1:
                spec_c = fft_lp_filter(spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"])
            else:
                gp = 1
                for b in range(mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]):
                    g = math.pow(10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0)
                    gp = g
                    spec_c[:, b, :] *= g

    return np.asfortranarray(spec_c)


def wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False):

    if wave.ndim == 1:
        wave = np.asfortranarray([wave, wave])

    if not is_v51_model:
        if mp.param["reverse"]:
            wave_left = np.flip(np.asfortranarray(wave[0]))
            wave_right = np.flip(np.asfortranarray(wave[1]))
        elif mp.param["mid_side"]:
            wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
        elif mp.param["mid_side_b2"]:
            wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
        else:
            wave_left = np.asfortranarray(wave[0])
            wave_right = np.asfortranarray(wave[1])
    else:
        wave_left = np.asfortranarray(wave[0])
        wave_right = np.asfortranarray(wave[1])

    spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)
    spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)

    spec = np.asfortranarray([spec_left, spec_right])

    if is_v51_model:
        spec = convert_channels(spec, mp, band)

    return spec


def spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True):
    spec_left = np.asfortranarray(spec[0])
    spec_right = np.asfortranarray(spec[1])

    wave_left = librosa.istft(spec_left, hop_length=hop_length)
    wave_right = librosa.istft(spec_right, hop_length=hop_length)

    if is_v51_model:
        cc = mp.param["band"][band].get("convert_channels")
        if "mid_side_c" == cc:
            return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)])
        elif "mid_side" == cc:
            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
        elif "stereo_n" == cc:
            return np.asfortranarray([np.subtract(wave_left, wave_right * 0.25), np.subtract(wave_right, wave_left * 0.25)])
    else:
        if mp.param["reverse"]:
            return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
        elif mp.param["mid_side"]:
            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
        elif mp.param["mid_side_b2"]:
            return np.asfortranarray([np.add(wave_right / 1.25, 0.4 * wave_left), np.subtract(wave_left / 1.25, 0.4 * wave_right)])

    return np.asfortranarray([wave_left, wave_right])


def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False):
    bands_n = len(mp.param["band"])
    offset = 0

    for d in range(1, bands_n + 1):
        bp = mp.param["band"][d]
        spec_s = np.ndarray(shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex)
        h = bp["crop_stop"] - bp["crop_start"]
        spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[:, offset : offset + h, :]

        offset += h
        if d == bands_n:  # higher
            if extra_bins_h:  # if --high_end_process bypass
                max_bin = bp["n_fft"] // 2
                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]
            if bp["hpf_start"] > 0:
                if is_v51_model:
                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp["hpf_start"], bp["hpf_stop"] - 1)
                else:
                    spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
            if bands_n == 1:
                wave = spectrogram_to_wave(spec_s, bp["hl"], mp, d, is_v51_model)
            else:
                wave = np.add(wave, spectrogram_to_wave(spec_s, bp["hl"], mp, d, is_v51_model))
        else:
            sr = mp.param["band"][d + 1]["sr"]
            if d == 1:  # lower
                if is_v51_model:
                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp["lpf_start"], bp["lpf_stop"])
                else:
                    spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])

                try:
                    wave = librosa.resample(spectrogram_to_wave(spec_s, bp["hl"], mp, d, is_v51_model), orig_sr=bp["sr"], target_sr=sr, res_type=wav_resolution)
                except ValueError as e:
                    print(f"Error during resampling: {e}")
                    print(f"Spec_s shape: {spec_s.shape}, SR: {sr}, Res type: {wav_resolution}")

            else:  # mid
                if is_v51_model:
                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp["hpf_start"], bp["hpf_stop"] - 1)
                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp["lpf_start"], bp["lpf_stop"])
                else:
                    spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
                    spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])

                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp["hl"], mp, d, is_v51_model))

                try:
                    wave = librosa.resample(wave2, orig_sr=bp["sr"], target_sr=sr, res_type=wav_resolution)
                except ValueError as e:
                    print(f"Error during resampling: {e}")
                    print(f"Spec_s shape: {spec_s.shape}, SR: {sr}, Res type: {wav_resolution}")

    return wave


def get_lp_filter_mask(n_bins, bin_start, bin_stop):
    mask = np.concatenate([np.ones((bin_start - 1, 1)), np.linspace(1, 0, bin_stop - bin_start + 1)[:, None], np.zeros((n_bins - bin_stop, 1))], axis=0)

    return mask


def get_hp_filter_mask(n_bins, bin_start, bin_stop):
    mask = np.concatenate([np.zeros((bin_stop + 1, 1)), np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None], np.ones((n_bins - bin_start - 2, 1))], axis=0)

    return mask


def fft_lp_filter(spec, bin_start, bin_stop):
    g = 1.0
    for b in range(bin_start, bin_stop):
        g -= 1 / (bin_stop - bin_start)
        spec[:, b, :] = g * spec[:, b, :]

    spec[:, bin_stop:, :] *= 0

    return spec


def fft_hp_filter(spec, bin_start, bin_stop):
    g = 1.0
    for b in range(bin_start, bin_stop, -1):
        g -= 1 / (bin_start - bin_stop)
        spec[:, b, :] = g * spec[:, b, :]

    spec[:, 0 : bin_stop + 1, :] *= 0

    return spec


def spectrogram_to_wave_old(spec, hop_length=1024):
    if spec.ndim == 2:
        wave = librosa.istft(spec, hop_length=hop_length)
    elif spec.ndim == 3:
        spec_left = np.asfortranarray(spec[0])
        spec_right = np.asfortranarray(spec[1])

        wave_left = librosa.istft(spec_left, hop_length=hop_length)
        wave_right = librosa.istft(spec_right, hop_length=hop_length)
        wave = np.asfortranarray([wave_left, wave_right])

    return wave


def wave_to_spectrogram_old(wave, hop_length, n_fft):
    wave_left = np.asfortranarray(wave[0])
    wave_right = np.asfortranarray(wave[1])

    spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)
    spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)

    spec = np.asfortranarray([spec_left, spec_right])

    return spec


def mirroring(a, spec_m, input_high_end, mp):
    if "mirroring" == a:
        mirror = np.flip(np.abs(spec_m[:, mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10, :]), 1)
        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))

        return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)

    if "mirroring2" == a:
        mirror = np.flip(np.abs(spec_m[:, mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10, :]), 1)
        mi = np.multiply(mirror, input_high_end * 1.7)

        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)


def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
    aggr = aggressiveness["value"] * 2

    if aggr != 0:
        if is_non_accom_stem:
            aggr = 1 - aggr

        if np.any(aggr > 10) or np.any(aggr < -10):
            print(f"Warning: Extreme aggressiveness values detected: {aggr}")

        aggr = [aggr, aggr]

        if aggressiveness["aggr_correction"] is not None:
            aggr[0] += aggressiveness["aggr_correction"]["left"]
            aggr[1] += aggressiveness["aggr_correction"]["right"]

        for ch in range(2):
            mask[ch, : aggressiveness["split_bin"]] = np.power(mask[ch, : aggressiveness["split_bin"]], 1 + aggr[ch] / 3)
            mask[ch, aggressiveness["split_bin"] :] = np.power(mask[ch, aggressiveness["split_bin"] :], 1 + aggr[ch])

    return mask


def stft(wave, nfft, hl):
    wave_left = np.asfortranarray(wave[0])
    wave_right = np.asfortranarray(wave[1])
    spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)
    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)
    spec = np.asfortranarray([spec_left, spec_right])

    return spec


def istft(spec, hl):
    spec_left = np.asfortranarray(spec[0])
    spec_right = np.asfortranarray(spec[1])
    wave_left = librosa.istft(spec_left, hop_length=hl)
    wave_right = librosa.istft(spec_right, hop_length=hl)
    wave = np.asfortranarray([wave_left, wave_right])

    return wave


def spec_effects(wave, algorithm="Default", value=None):
    if np.isnan(wave).any() or np.isinf(wave).any():
        print(f"Warning: Detected NaN or infinite values in wave input. Shape: {wave.shape}")

    spec = [stft(wave[0], 2048, 1024), stft(wave[1], 2048, 1024)]
    if algorithm == "Min_Mag":
        v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
        wave = istft(v_spec_m, 1024)
    elif algorithm == "Max_Mag":
        v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
        wave = istft(v_spec_m, 1024)
    elif algorithm == "Default":
        wave = (wave[1] * value) + (wave[0] * (1 - value))
    elif algorithm == "Invert_p":
        X_mag = np.abs(spec[0])
        y_mag = np.abs(spec[1])
        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
        v_spec = spec[1] - max_mag * np.exp(1.0j * np.angle(spec[0]))
        wave = istft(v_spec, 1024)

    return wave


def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):
    wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)

    if wave.ndim == 1:
        wave = np.asfortranarray([wave, wave])

    return wave


def wave_to_spectrogram_no_mp(wave):

    spec = librosa.stft(wave, n_fft=2048, hop_length=1024)

    if spec.ndim == 1:
        spec = np.asfortranarray([spec, spec])

    return spec


def invert_audio(specs, invert_p=True):

    ln = min([specs[0].shape[2], specs[1].shape[2]])
    specs[0] = specs[0][:, :, :ln]
    specs[1] = specs[1][:, :, :ln]

    if invert_p:
        X_mag = np.abs(specs[0])
        y_mag = np.abs(specs[1])
        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
        v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
    else:
        specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
        v_spec = specs[0] - specs[1]

    return v_spec


def invert_stem(mixture, stem):
    mixture = wave_to_spectrogram_no_mp(mixture)
    stem = wave_to_spectrogram_no_mp(stem)
    output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))

    return -output.T


def ensembling(a, inputs, is_wavs=False):

    for i in range(1, len(inputs)):
        if i == 1:
            input = inputs[0]

        if is_wavs:
            ln = min([input.shape[1], inputs[i].shape[1]])
            input = input[:, :ln]
            inputs[i] = inputs[i][:, :ln]
        else:
            ln = min([input.shape[2], inputs[i].shape[2]])
            input = input[:, :, :ln]
            inputs[i] = inputs[i][:, :, :ln]

        if MIN_SPEC == a:
            input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)
        if MAX_SPEC == a:
            #input = np.array(np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), inputs[i], input), dtype=object)
            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)
            #max_spec = np.array([np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), s, specs[0]) for s in specs[1:]], dtype=object)[-1]

    # linear_ensemble
    # input = ensemble_wav(inputs, split_size=1)

    return input


def ensemble_for_align(waves):

    specs = []

    for wav in waves:
        spec = wave_to_spectrogram_no_mp(wav.T)
        specs.append(spec)

    wav_aligned = spectrogram_to_wave_no_mp(ensembling(MIN_SPEC, specs)).T
    wav_aligned = match_array_shapes(wav_aligned, waves[1], is_swap=True)

    return wav_aligned


def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path, is_wave=False, is_array=False):

    wavs_ = []

    if algorithm == AVERAGE:
        output = average_audio(audio_input)
        samplerate = 44100
    else:
        specs = []

        for i in range(len(audio_input)):
            wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
            wavs_.append(wave)
            spec = wave if is_wave else wave_to_spectrogram_no_mp(wave)
            specs.append(spec)

        wave_shapes = [w.shape[1] for w in wavs_]
        target_shape = wavs_[wave_shapes.index(max(wave_shapes))]

        if is_wave:
            output = ensembling(algorithm, specs, is_wavs=True)
        else:
            output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))

        output = to_shape(output, target_shape.shape)

    sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)


def to_shape(x, target_shape):
    padding_list = []
    for x_dim, target_dim in zip(x.shape, target_shape):
        pad_value = target_dim - x_dim
        pad_tuple = (0, pad_value)
        padding_list.append(pad_tuple)

    return np.pad(x, tuple(padding_list), mode="constant")


def to_shape_minimize(x: np.ndarray, target_shape):

    padding_list = []
    for x_dim, target_dim in zip(x.shape, target_shape):
        pad_value = target_dim - x_dim
        pad_tuple = (0, pad_value)
        padding_list.append(pad_tuple)

    return np.pad(x, tuple(padding_list), mode="constant")


def detect_leading_silence(audio, sr, silence_threshold=0.007, frame_length=1024):
    """
    Detect silence at the beginning of an audio signal.

    :param audio: np.array, audio signal
    :param sr: int, sample rate
    :param silence_threshold: float, magnitude threshold below which is considered silence
    :param frame_length: int, the number of samples to consider for each check

    :return: float, duration of the leading silence in milliseconds
    """

    if len(audio.shape) == 2:
        # If stereo, pick the channel with more energy to determine the silence
        channel = np.argmax(np.sum(np.abs(audio), axis=1))
        audio = audio[channel]

    for i in range(0, len(audio), frame_length):
        if np.max(np.abs(audio[i : i + frame_length])) > silence_threshold:
            return (i / sr) * 1000

    return (len(audio) / sr) * 1000


def adjust_leading_silence(target_audio, reference_audio, silence_threshold=0.01, frame_length=1024):
    """
    Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.

    :param target_audio: np.array, audio signal that will have its silence adjusted
    :param reference_audio: np.array, audio signal used as a reference
    :param sr: int, sample rate
    :param silence_threshold: float, magnitude threshold below which is considered silence
    :param frame_length: int, the number of samples to consider for each check

    :return: np.array, target_audio adjusted to have the same leading silence as reference_audio
    """

    def find_silence_end(audio):
        if len(audio.shape) == 2:
            # If stereo, pick the channel with more energy to determine the silence
            channel = np.argmax(np.sum(np.abs(audio), axis=1))
            audio_mono = audio[channel]
        else:
            audio_mono = audio

        for i in range(0, len(audio_mono), frame_length):
            if np.max(np.abs(audio_mono[i : i + frame_length])) > silence_threshold:
                return i
        return len(audio_mono)

    ref_silence_end = find_silence_end(reference_audio)
    target_silence_end = find_silence_end(target_audio)
    silence_difference = ref_silence_end - target_silence_end

    try:
        ref_silence_end_p = (ref_silence_end / 44100) * 1000
        target_silence_end_p = (target_silence_end / 44100) * 1000
        silence_difference_p = ref_silence_end_p - target_silence_end_p
        print("silence_difference: ", silence_difference_p)
    except Exception as e:
        pass

    if silence_difference > 0:  # Add silence to target_audio
        if len(target_audio.shape) == 2:  # stereo
            silence_to_add = np.zeros((target_audio.shape[0], silence_difference))
        else:  # mono
            silence_to_add = np.zeros(silence_difference)
        return np.hstack((silence_to_add, target_audio))
    elif silence_difference < 0:  # Remove silence from target_audio
        if len(target_audio.shape) == 2:  # stereo
            return target_audio[:, -silence_difference:]
        else:  # mono
            return target_audio[-silence_difference:]
    else:  # No adjustment needed
        return target_audio


def match_array_shapes(array_1: np.ndarray, array_2: np.ndarray, is_swap=False):

    if is_swap:
        array_1, array_2 = array_1.T, array_2.T

    # print("before", array_1.shape, array_2.shape)
    if array_1.shape[1] > array_2.shape[1]:
        array_1 = array_1[:, : array_2.shape[1]]
    elif array_1.shape[1] < array_2.shape[1]:
        padding = array_2.shape[1] - array_1.shape[1]
        array_1 = np.pad(array_1, ((0, 0), (0, padding)), "constant", constant_values=0)

    # print("after", array_1.shape, array_2.shape)

    if is_swap:
        array_1, array_2 = array_1.T, array_2.T

    return array_1


def match_mono_array_shapes(array_1: np.ndarray, array_2: np.ndarray):

    if len(array_1) > len(array_2):
        array_1 = array_1[: len(array_2)]
    elif len(array_1) < len(array_2):
        padding = len(array_2) - len(array_1)
        array_1 = np.pad(array_1, (0, padding), "constant", constant_values=0)

    return array_1


def change_pitch_semitones(y, sr, semitone_shift):
    factor = 2 ** (semitone_shift / 12)  # Convert semitone shift to factor for resampling
    y_pitch_tuned = []
    for y_channel in y:
        y_pitch_tuned.append(librosa.resample(y_channel, orig_sr=sr, target_sr=sr * factor, res_type=wav_resolution_float_resampling))
    y_pitch_tuned = np.array(y_pitch_tuned)
    new_sr = sr * factor
    return y_pitch_tuned, new_sr


def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):

    wav, sr = librosa.load(audio_file, sr=44100, mono=False)

    if wav.ndim == 1:
        wav = np.asfortranarray([wav, wav])

    if not is_time_correction:
        wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0]
    else:
        if is_pitch:
            wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
            wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
        else:
            wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)
            wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)

        if wav_1.shape > wav_2.shape:
            wav_2 = to_shape(wav_2, wav_1.shape)
        if wav_1.shape < wav_2.shape:
            wav_1 = to_shape(wav_1, wav_2.shape)

        wav_mix = np.asfortranarray([wav_1, wav_2])

    sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)
    save_format(export_path)


def average_audio(audio):

    waves = []
    wave_shapes = []
    final_waves = []

    for i in range(len(audio)):
        wave = librosa.load(audio[i], sr=44100, mono=False)
        waves.append(wave[0])
        wave_shapes.append(wave[0].shape[1])

    wave_shapes_index = wave_shapes.index(max(wave_shapes))
    target_shape = waves[wave_shapes_index]
    waves.pop(wave_shapes_index)
    final_waves.append(target_shape)

    for n_array in waves:
        wav_target = to_shape(n_array, target_shape.shape)
        final_waves.append(wav_target)

    waves = sum(final_waves)
    waves = waves / len(audio)

    return waves


def average_dual_sources(wav_1, wav_2, value):

    if wav_1.shape > wav_2.shape:
        wav_2 = to_shape(wav_2, wav_1.shape)
    if wav_1.shape < wav_2.shape:
        wav_1 = to_shape(wav_1, wav_2.shape)

    wave = (wav_1 * value) + (wav_2 * (1 - value))

    return wave


def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):

    if wav_1.shape > wav_2.shape:
        wav_2 = to_shape(wav_2, wav_1.shape)
    if wav_1.shape < wav_2.shape:
        ln = min([wav_1.shape[1], wav_2.shape[1]])
        wav_2 = wav_2[:, :ln]

    ln = min([wav_1.shape[1], wav_2.shape[1]])
    wav_1 = wav_1[:, :ln]
    wav_2 = wav_2[:, :ln]

    return wav_2


def reshape_sources_ref(wav_1_shape, wav_2: np.ndarray):

    if wav_1_shape > wav_2.shape:
        wav_2 = to_shape(wav_2, wav_1_shape)

    return wav_2


def combine_arrarys(audio_sources, is_swap=False):
    source = np.zeros_like(max(audio_sources, key=np.size))

    for v in audio_sources:
        v = match_array_shapes(v, source, is_swap=is_swap)
        source += v

    return source


def combine_audio(paths: list, audio_file_base=None, wav_type_set="FLOAT", save_format=None):

    source = combine_arrarys([load_audio(i) for i in paths])
    save_path = f"{audio_file_base}_combined.wav"
    sf.write(save_path, source.T, 44100, subtype=wav_type_set)
    save_format(save_path)


def reduce_mix_bv(inst_source, voc_source, reduction_rate=0.9):
    # Reduce the volume
    inst_source = inst_source * (1 - reduction_rate)

    mix_reduced = combine_arrarys([inst_source, voc_source], is_swap=True)

    return mix_reduced


def organize_inputs(inputs):
    input_list = {"target": None, "reference": None, "reverb": None, "inst": None}

    for i in inputs:
        if i.endswith("_(Vocals).wav"):
            input_list["reference"] = i
        elif "_RVC_" in i:
            input_list["target"] = i
        elif i.endswith("reverbed_stem.wav"):
            input_list["reverb"] = i
        elif i.endswith("_(Instrumental).wav"):
            input_list["inst"] = i

    return input_list


def check_if_phase_inverted(wav1, wav2, is_mono=False):
    # Load the audio files
    if not is_mono:
        wav1 = np.mean(wav1, axis=0)
        wav2 = np.mean(wav2, axis=0)

    # Compute the correlation
    correlation = np.corrcoef(wav1[:1000], wav2[:1000])

    return correlation[0, 1] < 0


def align_audio(
    file1,
    file2,
    file2_aligned,
    file_subtracted,
    wav_type_set,
    is_save_aligned,
    command_Text,
    save_format,
    align_window: list,
    align_intro_val: list,
    db_analysis: tuple,
    set_progress_bar,
    phase_option,
    phase_shifts,
    is_match_silence,
    is_spec_match,
):

    global progress_value
    progress_value = 0
    is_mono = False

    def get_diff(a, b):
        corr = np.correlate(a, b, "full")
        diff = corr.argmax() - (b.shape[0] - 1)

        return diff

    def progress_bar(length):
        global progress_value
        progress_value += 1

        if (0.90 / length * progress_value) >= 0.9:
            length = progress_value + 1

        set_progress_bar(0.1, (0.9 / length * progress_value))

    # read tracks

    if file1.endswith(".mp3") and is_macos:
        length1 = rerun_mp3(file1)
        wav1, sr1 = librosa.load(file1, duration=length1, sr=44100, mono=False)
    else:
        wav1, sr1 = librosa.load(file1, sr=44100, mono=False)

    if file2.endswith(".mp3") and is_macos:
        length2 = rerun_mp3(file2)
        wav2, sr2 = librosa.load(file2, duration=length2, sr=44100, mono=False)
    else:
        wav2, sr2 = librosa.load(file2, sr=44100, mono=False)

    if wav1.ndim == 1 and wav2.ndim == 1:
        is_mono = True
    elif wav1.ndim == 1:
        wav1 = np.asfortranarray([wav1, wav1])
    elif wav2.ndim == 1:
        wav2 = np.asfortranarray([wav2, wav2])

    # Check if phase is inverted
    if phase_option == AUTO_PHASE:
        if check_if_phase_inverted(wav1, wav2, is_mono=is_mono):
            wav2 = -wav2
    elif phase_option == POSITIVE_PHASE:
        wav2 = +wav2
    elif phase_option == NEGATIVE_PHASE:
        wav2 = -wav2

    if is_match_silence:
        wav2 = adjust_leading_silence(wav2, wav1)

    wav1_length = int(librosa.get_duration(y=wav1, sr=44100))
    wav2_length = int(librosa.get_duration(y=wav2, sr=44100))

    if not is_mono:
        wav1 = wav1.transpose()
        wav2 = wav2.transpose()

    wav2_org = wav2.copy()

    command_Text("Processing files... \n")
    seconds_length = min(wav1_length, wav2_length)

    wav2_aligned_sources = []

    for sec_len in align_intro_val:
        # pick a position at 1 second in and get diff
        sec_seg = 1 if sec_len == 1 else int(seconds_length // sec_len)
        index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100

        if is_mono:
            samp1, samp2 = wav1[index : index + sr1], wav2[index : index + sr1]
            diff = get_diff(samp1, samp2)
            # print(f"Estimated difference: {diff}\n")
        else:
            index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100
            samp1, samp2 = wav1[index : index + sr1, 0], wav2[index : index + sr1, 0]
            samp1_r, samp2_r = wav1[index : index + sr1, 1], wav2[index : index + sr1, 1]
            diff, diff_r = get_diff(samp1, samp2), get_diff(samp1_r, samp2_r)
            # print(f"Estimated difference Left Channel: {diff}\nEstimated difference Right Channel: {diff_r}\n")

        # make aligned track 2
        if diff > 0:
            zeros_to_append = np.zeros(diff) if is_mono else np.zeros((diff, 2))
            wav2_aligned = np.append(zeros_to_append, wav2_org, axis=0)
        elif diff < 0:
            wav2_aligned = wav2_org[-diff:]
        else:
            wav2_aligned = wav2_org
            # command_Text(f"Audio files already aligned.\n")

        if not any(np.array_equal(wav2_aligned, source) for source in wav2_aligned_sources):
            wav2_aligned_sources.append(wav2_aligned)

    # print("Unique Sources: ", len(wav2_aligned_sources))

    unique_sources = len(wav2_aligned_sources)

    sub_mapper_big_mapper = {}

    for s in wav2_aligned_sources:
        wav2_aligned = match_mono_array_shapes(s, wav1) if is_mono else match_array_shapes(s, wav1, is_swap=True)

        if align_window:
            wav_sub = time_correction(
                wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts
            )
            wav_sub_size = np.abs(wav_sub).mean()
            sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size: wav_sub}}
        else:
            wav2_aligned = wav2_aligned * np.power(10, db_analysis[0] / 20)
            db_range = db_analysis[1]

            for db_adjustment in db_range:
                # Adjust the dB of track2
                s_adjusted = wav2_aligned * (10 ** (db_adjustment / 20))
                wav_sub = wav1 - s_adjusted
                wav_sub_size = np.abs(wav_sub).mean()
                sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size: wav_sub}}

        # print(sub_mapper_big_mapper.keys(), min(sub_mapper_big_mapper.keys()))

    sub_mapper_value_list = list(sub_mapper_big_mapper.values())

    if is_spec_match and len(sub_mapper_value_list) >= 2:
        # print("using spec ensemble with align")
        wav_sub = ensemble_for_align(list(sub_mapper_big_mapper.values()))
    else:
        # print("using linear ensemble with align")
        wav_sub = ensemble_wav(list(sub_mapper_big_mapper.values()))

    # print(f"Mix Mean: {np.abs(wav1).mean()}\nInst Mean: {np.abs(wav2).mean()}")
    # print('Final: ', np.abs(wav_sub).mean())
    wav_sub = np.clip(wav_sub, -1, +1)

    command_Text(f"Saving inverted track... ")

    if is_save_aligned or is_spec_match:
        wav1 = match_mono_array_shapes(wav1, wav_sub) if is_mono else match_array_shapes(wav1, wav_sub, is_swap=True)
        wav2_aligned = wav1 - wav_sub

        if is_spec_match:
            if wav1.ndim == 1 and wav2.ndim == 1:
                wav2_aligned = np.asfortranarray([wav2_aligned, wav2_aligned]).T
                wav1 = np.asfortranarray([wav1, wav1]).T

            wav2_aligned = ensemble_for_align([wav2_aligned, wav1])
            wav_sub = wav1 - wav2_aligned

        if is_save_aligned:
            sf.write(file2_aligned, wav2_aligned, sr1, subtype=wav_type_set)
            save_format(file2_aligned)

    sf.write(file_subtracted, wav_sub, sr1, subtype=wav_type_set)
    save_format(file_subtracted)


def phase_shift_hilbert(signal, degree):
    analytic_signal = hilbert(signal)
    return np.cos(np.radians(degree)) * analytic_signal.real - np.sin(np.radians(degree)) * analytic_signal.imag


def get_phase_shifted_tracks(track, phase_shift):
    if phase_shift == 180:
        return [track, -track]

    step = phase_shift
    end = 180 - (180 % step) if 180 % step == 0 else 181
    phase_range = range(step, end, step)

    flipped_list = [track, -track]
    for i in phase_range:
        flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)])

    return flipped_list


def time_correction(mix: np.ndarray, instrumental: np.ndarray, seconds_length, align_window, db_analysis, sr=44100, progress_bar=None, unique_sources=None, phase_shifts=NONE_P):
    # Function to align two tracks using cross-correlation

    def align_tracks(track1, track2):
        # A dictionary to store each version of track2_shifted and its mean absolute value
        shifted_tracks = {}

        # Loop to adjust dB of track2
        track2 = track2 * np.power(10, db_analysis[0] / 20)
        db_range = db_analysis[1]

        if phase_shifts == 190:
            track2_flipped = [track2]
        else:
            track2_flipped = get_phase_shifted_tracks(track2, phase_shifts)

        for db_adjustment in db_range:
            for t in track2_flipped:
                # Adjust the dB of track2
                track2_adjusted = t * (10 ** (db_adjustment / 20))
                corr = correlate(track1, track2_adjusted)
                delay = np.argmax(np.abs(corr)) - (len(track1) - 1)
                track2_shifted = np.roll(track2_adjusted, shift=delay)

                # Compute the mean absolute value of track2_shifted
                track2_shifted_sub = track1 - track2_shifted
                mean_abs_value = np.abs(track2_shifted_sub).mean()

                # Store track2_shifted and its mean absolute value in the dictionary
                shifted_tracks[mean_abs_value] = track2_shifted

        # Return the version of track2_shifted with the smallest mean absolute value

        return shifted_tracks[min(shifted_tracks.keys())]

    # Make sure the audio files have the same shape

    assert mix.shape == instrumental.shape, f"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}"

    seconds_length = seconds_length // 2

    sub_mapper = {}

    progress_update_interval = 120
    total_iterations = 0

    if len(align_window) > 2:
        progress_update_interval = 320

    for secs in align_window:
        step = secs / 2
        window_size = int(sr * secs)
        step_size = int(sr * step)

        if len(mix.shape) == 1:
            total_mono = (len(range(0, len(mix) - window_size, step_size)) // progress_update_interval) * unique_sources
            total_iterations += total_mono
        else:
            total_stereo_ = len(range(0, len(mix[:, 0]) - window_size, step_size)) * 2
            total_stereo = (total_stereo_ // progress_update_interval) * unique_sources
            total_iterations += total_stereo

    # print(total_iterations)

    for secs in align_window:
        sub = np.zeros_like(mix)
        divider = np.zeros_like(mix)
        step = secs / 2
        window_size = int(sr * secs)
        step_size = int(sr * step)
        window = np.hanning(window_size)

        # For the mono case:
        if len(mix.shape) == 1:
            # The files are mono
            counter = 0
            for i in range(0, len(mix) - window_size, step_size):
                counter += 1
                if counter % progress_update_interval == 0:
                    progress_bar(total_iterations)
                window_mix = mix[i : i + window_size] * window
                window_instrumental = instrumental[i : i + window_size] * window
                window_instrumental_aligned = align_tracks(window_mix, window_instrumental)
                sub[i : i + window_size] += window_mix - window_instrumental_aligned
                divider[i : i + window_size] += window
        else:
            # The files are stereo
            counter = 0
            for ch in range(mix.shape[1]):
                for i in range(0, len(mix[:, ch]) - window_size, step_size):
                    counter += 1
                    if counter % progress_update_interval == 0:
                        progress_bar(total_iterations)
                    window_mix = mix[i : i + window_size, ch] * window
                    window_instrumental = instrumental[i : i + window_size, ch] * window
                    window_instrumental_aligned = align_tracks(window_mix, window_instrumental)
                    sub[i : i + window_size, ch] += window_mix - window_instrumental_aligned
                    divider[i : i + window_size, ch] += window

        # Normalize the result by the overlap count
        sub = np.where(divider > 1e-6, sub / divider, sub)
        sub_size = np.abs(sub).mean()
        sub_mapper = {**sub_mapper, **{sub_size: sub}}

    # print("SUB_LEN", len(list(sub_mapper.values())))

    sub = ensemble_wav(list(sub_mapper.values()), split_size=12)

    return sub


def ensemble_wav(waveforms, split_size=240):
    # Create a dictionary to hold the thirds of each waveform and their mean absolute values
    waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)}

    # Initialize the final waveform
    final_waveform = []

    # For chunk
    for third_idx in range(split_size):
        # Compute the mean absolute value of each third from each waveform
        means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))]

        # Find the index of the waveform with the lowest mean absolute value for this third
        min_index = np.argmin(means)

        # Add the least noisy third to the final waveform
        final_waveform.append(waveform_thirds[min_index][third_idx])

    # Concatenate all the thirds to create the final waveform
    final_waveform = np.concatenate(final_waveform)

    return final_waveform


def ensemble_wav_min(waveforms):
    for i in range(1, len(waveforms)):
        if i == 1:
            wave = waveforms[0]

        ln = min(len(wave), len(waveforms[i]))
        wave = wave[:ln]
        waveforms[i] = waveforms[i][:ln]

        wave = np.where(np.abs(waveforms[i]) <= np.abs(wave), waveforms[i], wave)

    return wave


def align_audio_test(wav1, wav2, sr1=44100):
    def get_diff(a, b):
        corr = np.correlate(a, b, "full")
        diff = corr.argmax() - (b.shape[0] - 1)
        return diff

    # read tracks
    wav1 = wav1.transpose()
    wav2 = wav2.transpose()

    # print(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")

    wav2_org = wav2.copy()

    # pick a position at 1 second in and get diff
    index = sr1  # *seconds_length  # 1 second in, assuming sr1 = sr2 = 44100
    samp1 = wav1[index : index + sr1, 0]  # currently use left channel
    samp2 = wav2[index : index + sr1, 0]
    diff = get_diff(samp1, samp2)

    # make aligned track 2
    if diff > 0:
        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)
    elif diff < 0:
        wav2_aligned = wav2_org[-diff:]
    else:
        wav2_aligned = wav2_org

    return wav2_aligned


def load_audio(audio_file):
    wav, sr = librosa.load(audio_file, sr=44100, mono=False)

    if wav.ndim == 1:
        wav = np.asfortranarray([wav, wav])

    return wav


def rerun_mp3(audio_file):
    with audioread.audio_open(audio_file) as f:
        track_length = int(f.duration)

    return track_length
